Tom*_*Tom 6 merge r class rbind rbindlist
我想合并一大组数据框(大约 30 个),每个数据框有大约 200 个变量。这些数据集非常相似但不完全相同。
请在下面找到两个示例数据框:
library(data.table)
library(haven)
df1 <- fread(
"A B C iso year
0 B 1 NLD 2009
1 A 2 NLD 2009
0 Y 3 AUS 2011
1 Q 4 AUS 2011
0 NA 7 NLD 2008
1 0 1 NLD 2008
0 1 3 AUS 2012",
header = TRUE
)
df2 <- fread(
"A B D E iso year
0 1 1 NA ECU 2009
1 0 2 0 ECU 2009
0 0 3 0 BRA 2011
1 0 4 0 BRA 2011
0 1 7 NA ECU 2008
1 0 1 0 ECU 2008
0 0 3 2 BRA 2012
1 0 4 NA BRA 2012",
header = TRUE
)
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要重新创建错误:
class(df2$B) <- "anything"
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当我执行以下操作时
df_merged <- rbindlist(list(df1, df2), fill=TRUE, use.names=TRUE)
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数据集给出了错误:
Error in rbindlist(list(df1, df2), fill = TRUE, use.names = TRUE) :
Class attribute on column 2 of item 2 does not match with column 2 of item 1.
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我可以做什么:
rbindlist跳过不匹配,并添加一些后缀列。选项 1 的预期结果:
df_merged <- fread(
"A B B.x C D E iso year
0 A NA 1 NA NA NLD 2009
1 Y NA 2 NA NA NLD 2009
0 Q NA 3 NA NA AUS 2011
1 NA NA 4 NA NA AUS 2011
0 0 NA 7 NA NA NLD 2008
1 1 NA 1 NA NA NLD 2008
0 1 NA 3 NA NA AUS 2012
0 NA 1 NA 1 NA ECU 2009
1 NA 0 NA 2 0 ECU 2009
0 NA 0 NA 3 0 BRA 2011
1 NA 0 NA 4 0 BRA 2011
0 NA 1 NA 7 NA ECU 2008
1 NA 0 NA 1 0 ECU 2008
0 NA 0 NA 3 2 BRA 2012
1 NA 0 NA 4 NA BRA 2012",
header = TRUE
)
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选项 2 的预期结果:
df_merged <- fread(
"A B C D E iso year
0 3 1 NA NA NLD 2009
1 4 2 NA NA NLD 2009
0 5 3 NA NA AUS 2011
1 5 4 NA NA AUS 2011
0 0 7 NA NA NLD 2008
1 1 1 NA NA NLD 2008
0 1 3 NA NA AUS 2012
0 1 NA 1 NA ECU 2009
1 0 NA 2 0 ECU 2009
0 0 NA 3 0 BRA 2011
1 0 NA 4 0 BRA 2011
0 1 NA 7 NA ECU 2008
1 0 NA 1 0 ECU 2008
0 0 NA 3 2 BRA 2012
1 0 NA 4 NA BRA 2012",",
header = TRUE
)
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我想出了这个绕过这个问题的不优雅的解决方案。基本上,我正在做的是将列表第一项的列的属性分配给与列表中所有其他项目同名的列。请记住,此解决方案是有问题的,并且根据项目的不同,它可能是一种非常错误的做法,因为它有可能弄乱您的数据。但是,如果您需要的是用来rbindlist组合数据帧,那么这就是诀窍
dfs <- list(df1, df2)
varnames <- names(dfs[[1]]) # variable names
vattr <- purrr::map_chr(varnames, ~class(dfs[[1]][[.x]])) # variable attributes
for (i in seq_along(dfs)) {
# assign the same attributes of list 1 to the rest of the lists
for (j in seq_along(varnames)) {
if (varnames[[j]] %in% names(dfs[[i]])) {
class(dfs[[i]][[varnames[[j]]]]) <- vattr[[j]]
}
}
}
df_merged <- data.table::rbindlist(dfs, fill=TRUE, use.names=TRUE)
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最好的,